0000000001229643

AUTHOR

Payam Vahdani Amoli

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Unsupervised network intrusion detection systems for zero-day fast-spreading network attacks and botnets

2015

Today, the occurrence of zero-day and complex attacks in high-speed networks is increasingly common due to the high number vulnerabilities in the cyber world. As a result, intrusions become more sophisticated and fast to detrimental the networks and hosts. Due to these reasons real-time monitoring, processing and intrusion detection are now among the key features of NIDS. Traditional types of intrusion detection systems such as signature base IDS are not able detect intrusions with new and complex strategies. Now days, automatic traffic analysis and anomaly intrusion detection became more efficient in field of network security however they suffer from high number of false alarms. Among all …

tunkeilijan havaitsemisjärjestelmätintrusion detectionmonitorointitietoliikenneverkottiedonsiirtoanomaly detectionreaaliaikaisuusmachine learningclustering (unsupervised)koneoppiminenalgoritmitnetwork securityklusterianalyysitietoturvaverkkohyökkäykset
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